"""
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PointHMR Official Code
Copyright (c) Deep Computer Vision Lab. (DCVL.) All Rights Reserved
Licensed under the MIT license.
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Modified from FastMETRO (https://github.com/postech-ami/FastMETRO)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved [see https://github.com/postech-ami/FastMETRO/blob/main/LICENSE for details]
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Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved [see https://github.com/facebookresearch/detr/blob/main/LICENSE for details]
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"""


import math
import torch
from torch import nn

class PositionEmbeddingSine(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one
    used by the Attention is all you need paper, generalized to work on images.
    """
    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
        super().__init__()
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale

    def forward(self, bs, h, w, device):
        ones = torch.ones((bs, h, w), dtype=torch.bool, device=device)
        y_embed = ones.cumsum(1, dtype=torch.float32)
        x_embed = ones.cumsum(2, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale

        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=device)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)

        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos


def build_position_encoding(hidden_dim):
    N_steps = hidden_dim // 2

    position_embedding = PositionEmbeddingSine(N_steps, normalize=True)

    return position_embedding